Optimal fault location

Basic goal of power system is to continuously provide electrical energy to the users. Like with any other system, failures in power system can occur. In those situations it is critical that correct remedial actions are applied as soon as possible after the accurate fault condition and location are detected. This thesis has been focusing on automated fault location procedure. Different fault location algorithms, classified according to the spatial placement of physical measurements on single ended, multiple ended and sparse system-wide, are investigated. As outcome of this review, methods are listed as function of different parameters that influence their accuracy. This comparison is than used for generating procedure for optimal fault location algorithm selection. According to available data, and position of the fault with respect to the data, proposed procedure decides between different algorithms and selects an optimal one. A new approach is developed by utilizing different data structures such as binary tree and serialization in order to efficiently implement algorithm decision engine. After accuracy of algorithms is strongly influenced by available input data, different data sources are recommended in proposed architecture such as the digital fault recorders, circuit breaker monitoring, SCADA, power system model and etc. Algorithm for determining faulted section is proposed based on the data from circuit breaker monitoring devices. This algorithm works in real time by recognizing to which sequence of events newly obtained recording belongs. Software prototype of the proposed automated fault location analysis is developed using Java programming language. Fault location analysis is automatically triggered by appearance of new event files in a specific folder. The tests were carried out using the real life transmission system as an example

Monitoring of microbial dynamics in a drinking water distribution system using the culture-free, user-friendly, MYcrobiota platform

Drinking water utilities currently rely on a range of microbiological detection techniques to evaluate the quality of their drinking water (DW). However, microbiota profiling using culture-free 16S rRNA gene next-generation sequencing (NGS) provides an opportunity for improved monitoring of the microbial ecology and quality of DW. Here, we evaluated the utility of a previously validated microbiota profiling platform (MYcrobiota) to investigate the microbial dynamics of a full-scale, non-chlorinated DW distribution system (DWDS). In contrast to conventional methods, we observed spatial and temporal bacterial genus changes (expressed as operational taxonomic units - OTUs) within the DWDS. Further, a small subset of bacterial OTUs dominated with abundances that shifted across the length of the DWDS, and were particularly affected by a post-disinfection step. We also found seasonal variation in OTUs within the DWDS and that many OTUs could not be identified, even though MYcrobiota is specifically designed to reduce potential PCR sequencing artefacts. This suggests that our current knowledge about the microbial ecology of DW communities is limited. Our findings demonstrate that the user-friendly MYcrobiota platform facilitates culture-free, standardized microbial dynamics monitoring and has the capacity to facilitate the introduction of microbiota profiling into the management of drinking water quality